{"id":"W4408562534","doi":"10.2139/ssrn.5180947","title":"AI-Generated Summaries as Differentiating Reference: Impact on User Content Generation in Online Communities","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Quest University Canada; Sciencetech (Canada)","funders":"","keywords":"User-generated content; Content (measure theory); Computer science; Information retrieval; World Wide Web; Mathematics; Social media","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005803222,0.0004490277,0.0005518871,0.0032251,0.0008581456,0.005023461,0.000733504,0.00127503,0.007770092],"category_scores_gemma":[0.0899526,0.0001697942,0.0004326577,0.002533143,0.0004843082,0.004113194,0.001751112,0.000881665,0.002042202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006222205,"about_ca_system_score_gemma":0.0008771427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223084,"about_ca_topic_score_gemma":0.002030728,"domain_scores_codex":[0.9947634,0.002920543,0.0004162764,0.0005519838,0.001197086,0.0001506637],"domain_scores_gemma":[0.8782025,0.09723999,0.00608523,0.005252654,0.009479309,0.00374026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01613688,0.003259054,0.2082229,0.002392749,0.0006030616,0.0005949091,0.01524584,0.005797848,0.02655119,0.006745869,0.009254828,0.7051949],"study_design_scores_gemma":[0.001716724,0.02011622,0.5851541,0.001051208,0.003595565,0.002049147,0.02790199,0.2154136,0.0573529,0.03269365,0.05227765,0.0006773581],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722286,0.001164398,0.007526453,0.0005870337,0.0001696277,0.0002725618,0.0008652058,0.001496605,0.01568941],"genre_scores_gemma":[0.9869078,0.0002936653,0.008705244,0.00007297808,0.0001035015,0.00007145103,0.0008207772,0.000162293,0.002862219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007770092,"threshold_uncertainty_score":0.03069079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04552487465689917,"score_gpt":0.3436870209861063,"score_spread":0.2981621463292071,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}